How Embeddings Shape Graph Neural Networks: Classical vs Quantum-Oriented Node Representations

Nouhaila Innan, Antonello Rosato, Alberto Marchisio, Muhammad Shafique|April 16, 2026arXiv

Key Takeaway

When choosing node embeddings for graph neural networks, quantum-oriented representations offer consistent improvements on molecular and structural datasets, but classical baselines are still optimal for social graphs—the choice depends heavily on your data type.

Summary

This paper compares different ways to represent nodes in graph neural networks, testing classical embeddings against quantum-inspired alternatives on standard benchmarks.

evaluation architecture

Key Terms

node-embedding graph-neural-network inductive-bias variational-embedding